Connected Vehicle Pooled Fund Study – Research

Program to Support the Development and Deployment of Infrastructure Based Connected Vehicle Applications

Current Projects

Currently, two projects are being sponsored by the pooled fund study. Brief descriptions of these projects are provided below:

5.9 GHz Dedicated Short Range Communication Vehicle-Based Road and Weather Condition Application: Phase II

Conducted by Synesis Partners LLC (PI: Kyle Garrett)

The objective of this research project is to develop and test the acquisition of road and weather condition information from 5.9 GHz DSRC-equipped public agency vehicles; to transmit this data via RSE to a central server; and ultimately to store it for use by agency maintenance personnel. Phase 1 has been completed and developed a suite of onboard and roadside equipment and software to enable data gathering from vehicles to roadside collectors. The system captures data on the vehicle’s on-board equipment (OBE) from diverse sources, and transmits the data over DSRC to the roadside equipment (RSE). This Phase 2 project will build off of Phase 1 and provide data aggregation from the roadside to back-office servers with end-to-end system testing. In addition, the developed system will be extended to Michigan as well as New York State.

Tasks

  • Task 1 – Requirements Development – completed in Phase I
  • Task 2 – Concept of Operations – completed in Phase I
  • Task 3 – Application Development
  • Task 4 – Application Installation and Testing (New York State)
  • Task 5 – Application Installation and Testing (Michigan)
  • Task 6 – CV Application Compatibility

Basic Infrastructure Message Development and Standards Support

Conducted by Southwest Research Institute (PI: Michael Brown)

The goals of this project are to develop a Basic Infrastructure Message (BIM); and to establish a means to collaborate with the relevant standards development organizations. While the standards (such as SAE J2735) of various messages for DSRC equipped vehicles are fairly well defined, from the infrastructure side, which infrastructure information will be or needs to be broadcasted is relatively unknown and has not been well investigated yet. With this, it is needed to develop a corollary message from the infrastructure, a Basic Infrastructure Message (BIM). Having a standard BIM would help the OEMs and third party application providers to understand that there will be some infrastructure for them to rely on, and will give them some basis for the kind of message they can expect from the infrastructure. In addition, this will also help the public transportation agencies to know what kind of information to broadcast from their Road Side Equipment (RSE).

Tasks

  • Task 1 – Project Management
  • Task 2 – Infrastructure Information Elements Review
  • Task 3 – Standards and Related Activities Review
  • Task 4 – Basic Infrastructure Message Development
  • Task 5 – Basic Infrastructure Message Standardization
  • Task 6 – Connected Vehicle Standards Support

Completed Projects

A total of ten projects were completed with the support from the pooled fund study. Note that, IntelliDrive, the previous name of Connected Vehicle, appears in the early stage projects. Brief descriptions and the final reports of these projects are presented below.

Multi-Modal Intelligent Traffic Signal System – Phase II: System Development, Deployment and Field Test (Final Report)

Conducted by University of Arizona (PI: Larry Head, klhead@email.arizona.edu), California PATH (PI: Wei Bin Zhang), Econolite (PI: Gary Duncan/Eric Raamot), Savari (PI: Roy Jose)

The University of Arizona has teamed with California PATH of the University of California at Berkeley (UCB) to design, develop, deploy, and field test a multi-modal intelligent traffic signal system that operates in a connected vehicle environment. The team was supported by technical experts from a connected vehicle system equipment manufacturer (Savari) and a traffic signal control system supplier (Econolite). The objectives of this project are:

  • To develop a detailed design, construct the software and hardware system, and conduct a field test of a comprehensive traffic signal system that services multiple modes of transportation, including general vehicles, transit, emergency vehicles, freight fleets and pedestrians.

Tasks

  • Task 1: Project Plan
  • Task 2: Detailed System and Software Design (Detailed Design – AZ, Detailed Design – CA)
  • Task 3: System and Software Development
  • Task 4: System Integration, Laboratory Testing
  • Task 5: Support Impact Assessment (IA) Contractor
  • Task 6: Field Integration and Testing (AZ)
  • Task 7: Field Integration and Testing (CA)
  • Task 8: System Test and Evaluation (AZ)
  • Task 9: System Test and Evaluation (CA)
  • Task 10: System Demonstration and Final Documentation (Final Report – note this final report contains the results from Tasks 3-10)

Best Practices for Surveying/Mapping Roadways and Intersections for Connected Vehicle Applications (Final Report)

Conducted by University of California, Riverside (PIs: Dr. Jay Farrell and Dr. Matthew Barth)

The goal of this project is to analyze and document the surveying and mapping requirements for expected connected vehicle applications, and to determine the best practices that should be used to satisfy them.  An emphasis was placed on efficiency, particularly with respect to lowering the costs and time required.  Here are some additional considerations:

  • Safety of personnel performing the work
  • Accuracy of the measurements
  • Minimal/no lane closures needed
  • Minimal time required to complete the work
  • Creation of maps that are easy to update as aspects of the location change

Tasks

  • Task 1: Mapping Methodology Assessment
  • Task 2: Mobile Mapping System Enhancements
  • Task 3: Map Representations
  • Task 4: Map Representation Updating
  • Task 5: Feature Extraction Methods
  • Task 6: Reporting

5.9 GHz Dedicated Short Range Communication Vehicle-Based Road and Weather Condition Application (Final Report)

Conducted by Synesis Partners LLC (PI: Kyle Garrett)

The objective of this research project is to develop and test the acquisition of road and weather condition information from 5.9 GHz DSRC-equipped public agency vehicles; to transmit this data via RSE to a central server; and ultimately to store it in the Clarus system for use by agency maintenance personnel. The project team is led by Kyle Garrett and Bryan Krueger of Synesis Partners LLC with Steve Kuciemba and Scott Shogan of Parsons Brinckerhoff and Sheldon Drobot from the National Center for Atmospheric Research.

Tasks

  • Task 1 – Requirements Development – describes the data elements and data sets desired for the road weather applications; determines what weather-related data are actually available on each of the vehicles; and identifies what additional sensors and equipment would be needed to provide the desired data sets. The output of this task is a messaging requirements specification.
  • Task 2 – Concept of Operations – develops a Concept of Operations (ConOps) for an application to provide 5.9 GHz DSRC-based road and weather condition data to the Clarus system. ConOps includes use cases, a description of the system architecture, and high-level system requirements.
  • Task 3 – Application Development – specifies the DSRC equipment, develops the OBE, RSE and data collection applications, and determines any adaptations needed to support integration with existing DSRC deployments on the Long Island Expressway and Spring Valley test beds in New York.
  • Task 4 – Application Installation and Testing – procures equipment, selects deployment sites, assembles and tests the system hardware and software, and deploys the system components.

Multi-Modal Intelligent Traffic Signal System – Phase I: Development of Concept of Operations, System Requirements, System Design and a Test Plan

Conducted by University of Arizona (PI: Larry Head, klhead@email.arizona.edu), California PATH (PI: Steve Shladover), Econolite (PI: Gary Duncan), Savari (PI: Ramesh Siripurapu), SCSC (PI: David Kelley), Kapsch (PI: Justin McNew), Volvo Technology (PI: Mike Siebert)

The University of Arizona has teamed with California PATH of the University of California at Berkeley (UCB) to design a multi-modal intelligent traffic signal system that will operate in a connected vehicle environment. The team is supported by technical experts from connected vehicle system equipment manufacturers (Savari and Kapsch), a traffic signal control system supplier (Econolite), an SAE J2735 communications standards expert (SCSC), and a commercial vehicle technology company (Volvo Technologies). The objectives of this project are:

  • To develop a concept of operations, systems requirements and system design for a comprehensive traffic signal system that services multiple modes of transportation, including general vehicles, transit, emergency vehicles, freight fleets and pedestrians; and
  • To prepare for field testing/demonstration of the developed Multi-Modal Intelligent Traffic Signal System.

Deliverables

Traffic Management Centers in a Connected Vehicle Environment

Conducted by Kimley-Horn and Associates, Inc. (PI: Lisa Burgess), Noblis (PI: Mike McGurrin), DGD Enterprises (PI: Dorothy Drinkwater), and Adams and Garth, Inc. (PI: Bernie Wagenblast).

Kimley-Horn and Associates, Inc. (KHA) has teamed with Noblis, DGD Enterprises, and Bernie Wagenblast to identify how a connected vehicle environment will shape the role and function of TMCs. This project is examining operational, technical and policy impacts of a new TMC environment and will inform the Pooled Fund Study (PFS) members about priority TMC opportunities, needs and gaps and that would need to be addressed in future TMCs. The objectives of this project are to:

  • Identify current connected vehicle activities that have the potential to have the highest impact on TMCs;
  • Identify what TMC functions or activities could most benefit from integrating connected vehicle data and capabilities;
  • Assess overall “readiness” of TMCs to adapt to a connected vehicle environment and identify challenges, constraints and potential timeframe considerations; and
  • Develop a concept for a future TMC within a connected vehicle environment to be able to proactively plan for future TMC operations, partnerships, and capabilities.

Deliverables

Aftermarket On-Board Equipment for Cooperative Transportation Systems: Enabling Accelerated Installation of Aftermarket On-Board Equipment for Cooperative Transportation Systems (Final Report)

Conducted by Visteon Corporation (PI:  Debby Bezzina, dbezzina@visteon.com), California PATH (PI: Ching-Yao Chan, cychan@path.berkeley.edu), and Industrial Technology Research Institute (PI:  Michael Li, hhli@itri.org.tw)

Visteon Corporation has teamed with Industrial Technology Research Institute (ITRI) and California PATH of the University of California at Berkeley (UCB) to address the critical question of how to expedite the installation of OBE units, which is a critical point facing the effectiveness of Connected Vehicle deployment, and to provide strategic recommendations in fostering rapid introduction of aftermarket OBEs. The objectives of this project are:

  • To analyze industry’s ability to manufacture dynamic configurable multi-band aftermarket OBE units;
  • To identify actions necessary to recue consumer cost of aftermarket OBE purchase; and
  • To identify actions needed to accelerate installation of aftermarket OBE units in the vehicle fleet.

Certification Program for Cooperative Transportation Systems: Preparing to Develop a Standards Compliance and Interoperability Certification Program for Cooperative Transportation Systems Hardware and Software (Final Report)

Conducted by OmniAir, PI: Timothy McGuckin (mcguckin@omniair.org)

As a USDOT national initiative, the ‘Connected Vehicle’ is part of the overall Intelligent Transportation System program as broadly planned at the federal level. However, the deployment of Cooperative System and Connected Vehicle technologies (which includes V2I and V2V) will be implemented at a local level with many of the intricacies of infrastructure management and interoperability falling to state & local transportation operating authorities. Within this context, it is important that the needs of these operating authorities are considered and reflected in initiative planning and implementation tactics.

A critical component to the overall success of national, interoperable Cooperative System and Connected Vehicle deployments is a robust certification process that ensures performance and interoperability of said technologies and systems. This project investigated the current landscape of certification activity in this space, assessed the needs of state and local infrastructure owners, and identified the gaps that may exist between current activity and state and local needs. The ultimate deliverable is actionable certification-related recommendations that further promote state & local efforts to deploy cooperative and connected transportation systems.

IntelliDrive Traffic Signal Control Algorithms (Final Report)

Completed by University of Virginia Center for Transportation Studies, PI: Dr. Brian L. Smith (briansmith@virginia.edu)

The effectiveness of traffic signal control algorithms depends primarily upon the control logic and the quality of the sensing infrastructure. Until now, with the deployment of fixed sensors, advanced traffic control algorithms were developed to use data available from point detectors. This data presents a number of limitations to the effectiveness of the systems. One such limitation in existing traffic controller logic is the lack of lane-by-lane gap out control feature (i.e., gap-out at actuated control is only determined by lane-group). Another limitation is the optimization approach taken in the existing algorithms. That is, an optimization of the traffic signal control relies on an estimation of performance measures (e.g., control delay defined in the Highway Capacity Manual) using vehicle counts, and do not use directly measure travel times.

As noted, current traffic signal control algorithms cannot take full advantage of the rich vehicular information that will be available from IntelliDrive. As such, the goal of this project is to develop and investigate expected benefits of traffic signal control algorithms designed specifically to take advantage of IntelliDrive. The objectives are:

  • To develop and evaluate new traffic signal control algorithms by fully utilizing new IntelliDrive data sources,
  • To develop tools for generating meaningful arterial Measures of Effectiveness (MOEs) from IntelliDrive data sources, and
  • To understand and document specific risks, constraints and opportunities of the developed algorithms in a large-scale deployment.

Investigation of Pavement Maintenance Support Applications of IntelliDrive (Final Report)

Completed by Auburn University, PI: Dr. David Bevly (dmbevly@eng.auburn.edu)

Maintenance of pavements represents one of the most important (and costly) functions of a transportation agency.  To do so, DOT’s must regularly measure/rate the quality of its pavements. Currently, this involves “manual” visual inspection and, in some cases, use of a specialized van with ultrasonic and video sensors to measure rutting and other pavement distress. In current practice, pavement assessment is conducted only periodically due to the limited availability of specialized equipment and the high cost.

The international roughness index (IRI) is a standardized pavement roughness measurement that was developed in the 1980s. IRI is usually calculated from longitudinal profile measurements obtained during pavement profiling surveys using specialized equipment. Another important measurement needed for pavement maintenance is detecting and locating (mapping) potholes. It is expected that, using IntelliDrive probe vehicles, pavement condition may be assessed with greater coverage in a timelier manner. Finally, the goal of this project is to investigate if vehicular data available from IntelliDrive can be used to measure the pavement condition. Specific objectives include:

  • To develop estimates of the International Roughness Index (IRI),
  • To detect and map potholes, and
  • To understand and document specific risks, constraints and opportunities in a large-scale deployment of the proposed system.

Investigating the Potential Benefits of Broadcasted Signal Phase and Timing (SPAT) Data under IntelliDrive (Final Report)

Completed by University of California PATH Program, PI: Steve Shladover (steve@path.berkeley.edu)

There exists potential to improve the traveler experience on urban arterials by providing Signal Phase and Timing (SPAT) data. With SPAT data, a driver would be able to adjust (and maintain) his/her speed – possibly with the help from an in-vehicle application – so that he/she can progress through intersections rather than having to change his speed frequently, i.e. accelerating in the middle of links, decelerating before the intersection, and accelerating again after the intersection. This may result in “eco-drive” type benefits such as less acceleration/deceleration which will culminate into less fuel consumptions as well as less vehicle emissions. There are also likely safety benefits that travelers may realize given the availability of SPAT data.

Considerable interest is growing in beginning the deployment of the infrastructure component of IntelliDrive by integrating Dedicated Short Range Communications (DSRC) in traffic controllers.  This would provide the capability to quickly begin to broadcast SPAT data for use by IntelliDrive equipped vehicles.  Hence an important need is to develop a concept of operations and to conduct high-level benefits assessment of applications that make use of SPAT data. With that, the goal of this project was set to investigate the potential benefits of using broadcasted SPAT data under an IntelliDrive environment. In order to accomplish the goal, several objectives identified are:

  • To identify the use cases of SPAT data
  • To develop concepts of operations for each of the identified use cases, and
  • To conduct high level benefits assessment.